One of many good presentations at this year’s MKS Umetrics users meeting in Chicago was given by Roland Bienert, Senior Scientist for process analytical technology (PAT ) and Sensors & Chemometrics at Sartorius Stedim Biotech. Bienert talked about various new applications of near infrared spectroscopy (NIR) for bioprocessing.
After the meeting, he discussed how he and his colleagues are using NIR and multivariate data analysis (MVDA) to enable improved bioprocess monitoring. Here is some of what he had to say.
PhM: What’s new in terms of applying NIR for bioprocess monitoring? Is anything being done now that wasn’t accepted practice a few years ago?
RB: In the early stage of the PAT initiative, people strove to measure single analytes online as accurately as they could using offline lab methods. That was, of course, hard to achieve, especially for low-concentration analytes such as glutamate or ammonia.
The new perspective is that NIR spectroscopy can do much more for process understanding. For instance, it is capable of measuring a metabolism sum-parameter, which does not rely on highly accurate single parameters but on the overall changes in spectra due to metabolite accumulation. And isn’t that what we are most interested in? This parameter has not been accessible via offline sampling.
In order to take the true Quality by Design (QbD) approach and learn about our processes, we do not necessarily need a number of isolated analyte concentrations. Instead, we need to combine the data we have acquired. This approach allows us to establish new tools like batch trajectories and endpoint determination for real-time release, which provide insights into the most important trends in an easy-to-interpret manner.
PhM: How about fermentation specifically—what improvements have been made in the application of NIR?
RB: From a technical point of view, it is a big advantage to be able to use a free beam spectrometer with a standard Ingold port adaptor, as we can today, instead of having to rely on fiber optics. The large free aperture, combined with a very high light yield and no moving parts within the spectrometer, results in excellent spectra quality throughout the whole fermentation. Even in extremely rough environments like large-scale bacterial fermentations, a free-beam spectrometer can automatically filter large fluctuations arising from air bubbles, for instance. This is possible since free beam spectrometers use diode array detectors instead of scanning techniques, which results in very fast measurements.
PhM: Do you see free-beam NIR as a substitute for fiber optic bioprocess monitoring, or do you see them each having strengths and weaknesses?
RB: With our new Ingold port adaptor, free beam spectrometers are surely a substitute for fiber optics NIR systems. Visiting the Achema 2012 fair recently, we observed a lot of progress in this field and a clear commitment to this kind of spectrometer. Comparing our system to fiber optic technologies such as MIR—as we have done recently in a project in cooperation with TCI Hannover—they are adding both their benefits to a complete package. The MIR system can be used to monitor small molecules, which can be used for feed control, whereas NIR in our solution is much more powerful in monitoring cell parameters and process trends.
PhM: Combined with MVDA, you can now use NIR to gain a much better sense of, for example, the activity of nutrients and metabolites during fermentation. What specific process parameters are more easily monitored today than even a few years ago?
RB: First of all, NIR spectra show a large number of variations which can be used to predict almost all the important parameters. The major challenge for vendors and manufacturers interested in NIR spectroscopy is to figure out which parameters can be directly measured by this technique and which are just based on correlations to some uninterpretable spectral changes. Therefore, Sartorius classifies between control parameters, which show directly changes within the spectra and are able to be discriminated from others.
Monitoring parameters, on the other hand, are in this context based on correlations and should be interpreted carefully. For parameter classification, Sartorius uses spiking experiments that break correlations between the analyte of interest and all the others.
With our approach, we can develop very strong predictions of important cell parameters, especially total cell count and viability. Those are both important during cell growth and production phase. Together with a reliable titer prediction, we have all important variables required to map the process during product formation.
PhM: What are some best practices for MVDA for fermentation processes? Or, conversely, what are some missed opportunities that you often see from manufacturers?
RB: Besides predicting the concentration of single analytes, MVDA can provide much more information from online data. Concerning NIR, for instance, we offer several tools for qualitative analysis of the process.
These tools are directly able to answer the most important questions, such as: Are the starting conditions consistent with earlier campaigns? Is the process alright? When is the perfect time for harvesting?
These are exactly the same questions that offline methods were supposed to answer several years ago, but they have not been suitable.
These questions can be perfectly addressed by qualitative process control tools. This starts with media classification just before inoculation in a statistically evaluated good-bad approach. The whole batch can be monitored as trajectories, which describe the process evolution and help to identify significant deviations in real time and thereby allow a guided sampling.